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智能手表加速度计分析和诊断震颤。

Smart watch accelerometry for analysis and diagnosis of tremor.

机构信息

Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Canada.

出版信息

J Neurosci Methods. 2014 Jun 15;230:1-4. doi: 10.1016/j.jneumeth.2014.04.021. Epub 2014 Apr 23.

Abstract

BACKGROUND

Distinguishing the postural re-emergent tremor of Parkinson disease from essential tremor can be difficult clinically. Use of accelerometry to aid diagnosis is limited to laboratory settings. We sought to record and differentiate these tremors using a smart watch device in an outpatient clinic.

NEW METHOD

41 patients were enrolled. Recordings were made with a smart watch device on the predominantly affected hand (all patients), and simultaneously with an analog accelerometer (10 patients) with hands at rest and outstretched. Tremor peak frequency, peak power, and power of the first four harmonics was calculated and compared between the two devices. Mean power at the first four harmonics was calculated and used to classify tremor as parkinsonian or essential. Test characteristics were calculated to compare the device and clinical diagnoses.

RESULTS

Mean harmonic peak power was both highly sensitive and specific for distinction of Parkinson disease postural tremor from essential tremor with an optimal threshold for our sample (sensitivity 90.9%, 95% CI 58.7-99.8%; specificity 100%, 95% CI 76.8-100%; Cohen's kappa=0.91, SE=0.08).

COMPARISON WITH EXISTING METHODS

The smart watch and analog devices had nearly perfect concordance of peak frequency and proportional harmonic power. The smart watch recordings in clinic took 3-6 min.

CONCLUSIONS

A smart watch device can provide accurate and diagnostically relevant information about postural tremor. Its portability and ease of use could help translate such techniques into routine clinic use or to the community.

摘要

背景

帕金森病的姿势性再出现性震颤与特发性震颤在临床上很难区分。使用加速度计辅助诊断仅限于实验室环境。我们试图使用智能手表设备在门诊环境中记录和区分这些震颤。

新方法

共纳入 41 名患者。使用智能手表设备在受影响较大的手(所有患者)上进行记录,并同时使用模拟加速度计(10 名患者)在静止和伸展的手上进行记录。计算并比较两种设备的震颤峰值频率、峰值功率和前四个谐波的功率。计算前四个谐波的平均功率,并用于将震颤分类为帕金森病或特发性。计算测试特征以比较设备和临床诊断。

结果

平均谐波峰值功率对于区分帕金森病姿势性震颤与特发性震颤具有高度的敏感性和特异性,对于我们的样本有最佳阈值(敏感性 90.9%,95%CI 58.7-99.8%;特异性 100%,95%CI 76.8-100%;Cohen's kappa=0.91,SE=0.08)。

与现有方法的比较

智能手表和模拟设备在峰值频率和比例谐波功率方面具有几乎完美的一致性。在诊所进行的智能手表记录需要 3-6 分钟。

结论

智能手表设备可以提供关于姿势性震颤的准确且具有诊断相关性的信息。其便携性和易用性可以帮助将这些技术转化为常规临床使用或社区使用。

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